Overview

Dataset statistics

Number of variables18
Number of observations43227
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 MiB
Average record size in memory176.4 B

Variable types

Numeric9
Categorical9

Alerts

age is highly overall correlated with Age GroupHigh correlation
month is highly overall correlated with contactHigh correlation
pdays is highly overall correlated with previous and 1 other fieldsHigh correlation
previous is highly overall correlated with pdaysHigh correlation
contact is highly overall correlated with monthHigh correlation
poutcome is highly overall correlated with pdaysHigh correlation
Age Group is highly overall correlated with ageHigh correlation
default is highly imbalanced (86.5%)Imbalance
poutcome is highly imbalanced (53.4%)Imbalance
job has 5022 (11.6%) zerosZeros
month has 2798 (6.5%) zerosZeros
campaign has 16723 (38.7%) zerosZeros
pdays has 35384 (81.9%) zerosZeros
previous has 35384 (81.9%) zerosZeros

Reproduction

Analysis started2023-07-16 04:06:24.016104
Analysis finished2023-07-16 04:07:02.538992
Duration38.52 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct77
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.809679
Minimum0
Maximum76
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T09:37:02.813753image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q115
median21
Q330
95-th percentile41
Maximum76
Range76
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.557529
Coefficient of variation (CV)0.46285302
Kurtosis0.32287076
Mean22.809679
Median Absolute Deviation (MAD)7
Skewness0.6853475
Sum985994
Variance111.46142
MonotonicityNot monotonic
2023-07-16T09:37:03.804441image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 1997
 
4.6%
13 1912
 
4.4%
15 1904
 
4.4%
16 1856
 
4.3%
17 1840
 
4.3%
18 1735
 
4.0%
12 1692
 
3.9%
19 1632
 
3.8%
21 1425
 
3.3%
20 1389
 
3.2%
Other values (67) 25845
59.8%
ValueCountFrequency (%)
0 12
 
< 0.1%
1 35
 
0.1%
2 48
 
0.1%
3 77
 
0.2%
4 127
 
0.3%
5 196
 
0.5%
6 296
 
0.7%
7 519
1.2%
8 781
1.8%
9 880
2.0%
ValueCountFrequency (%)
76 2
 
< 0.1%
75 1
 
< 0.1%
74 2
 
< 0.1%
73 2
 
< 0.1%
72 2
 
< 0.1%
71 3
 
< 0.1%
70 2
 
< 0.1%
69 3
 
< 0.1%
68 9
< 0.1%
67 3
 
< 0.1%

job
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3234784
Minimum0
Maximum11
Zeros5022
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T09:37:04.184329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q37
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2815767
Coefficient of variation (CV)0.7590131
Kurtosis-1.2808958
Mean4.3234784
Median Absolute Deviation (MAD)3
Skewness0.26456574
Sum186891
Variance10.768746
MonotonicityNot monotonic
2023-07-16T09:37:04.466584image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 9440
21.8%
4 8840
20.5%
9 7272
16.8%
0 5022
11.6%
7 4042
9.4%
5 2128
 
4.9%
6 1486
 
3.4%
2 1414
 
3.3%
10 1222
 
2.8%
3 1190
 
2.8%
Other values (2) 1171
 
2.7%
ValueCountFrequency (%)
0 5022
11.6%
1 9440
21.8%
2 1414
 
3.3%
3 1190
 
2.8%
4 8840
20.5%
5 2128
 
4.9%
6 1486
 
3.4%
7 4042
9.4%
8 902
 
2.1%
9 7272
16.8%
ValueCountFrequency (%)
11 269
 
0.6%
10 1222
 
2.8%
9 7272
16.8%
8 902
 
2.1%
7 4042
9.4%
6 1486
 
3.4%
5 2128
 
4.9%
4 8840
20.5%
3 1190
 
2.8%
2 1414
 
3.3%

marital
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1
25946 
2
12250 
0
5031 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43227
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 25946
60.0%
2 12250
28.3%
0 5031
 
11.6%

Length

2023-07-16T09:37:04.781673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T09:37:05.160682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 25946
60.0%
2 12250
28.3%
0 5031
 
11.6%

Most occurring characters

ValueCountFrequency (%)
1 25946
60.0%
2 12250
28.3%
0 5031
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43227
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25946
60.0%
2 12250
28.3%
0 5031
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Common 43227
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25946
60.0%
2 12250
28.3%
0 5031
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25946
60.0%
2 12250
28.3%
0 5031
 
11.6%

education
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1
22432 
2
12447 
0
6586 
3
 
1762

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43227
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 22432
51.9%
2 12447
28.8%
0 6586
 
15.2%
3 1762
 
4.1%

Length

2023-07-16T09:37:05.474954image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T09:37:05.835024image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 22432
51.9%
2 12447
28.8%
0 6586
 
15.2%
3 1762
 
4.1%

Most occurring characters

ValueCountFrequency (%)
1 22432
51.9%
2 12447
28.8%
0 6586
 
15.2%
3 1762
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43227
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22432
51.9%
2 12447
28.8%
0 6586
 
15.2%
3 1762
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 43227
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 22432
51.9%
2 12447
28.8%
0 6586
 
15.2%
3 1762
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 22432
51.9%
2 12447
28.8%
0 6586
 
15.2%
3 1762
 
4.1%

default
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
42415 
1
 
812

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43227
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 42415
98.1%
1 812
 
1.9%

Length

2023-07-16T09:37:06.149333image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T09:37:06.478788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 42415
98.1%
1 812
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 42415
98.1%
1 812
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43227
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42415
98.1%
1 812
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 43227
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42415
98.1%
1 812
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42415
98.1%
1 812
 
1.9%

balance
Real number (ℝ)

Distinct5661
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1758.8342
Minimum0
Maximum5660
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T09:37:06.807590image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile726
Q1974
median1317
Q32139
95-th percentile4427.7
Maximum5660
Range5660
Interquartile range (IQR)1165

Descriptive statistics

Standard deviation1138.2094
Coefficient of variation (CV)0.64713854
Kurtosis1.7361467
Mean1758.8342
Median Absolute Deviation (MAD)403
Skewness1.5349918
Sum76029126
Variance1295520.6
MonotonicityNot monotonic
2023-07-16T09:37:07.184510image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
914 3514
 
8.1%
915 195
 
0.5%
916 156
 
0.4%
918 139
 
0.3%
917 134
 
0.3%
919 113
 
0.3%
920 88
 
0.2%
922 81
 
0.2%
937 75
 
0.2%
924 69
 
0.2%
Other values (5651) 38663
89.4%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
5660 1
 
< 0.1%
5659 1
 
< 0.1%
5658 2
< 0.1%
5657 3
< 0.1%
5656 1
 
< 0.1%
5655 1
 
< 0.1%
5654 2
< 0.1%
5653 2
< 0.1%
5652 1
 
< 0.1%
5651 1
 
< 0.1%

housing
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1
24236 
0
18991 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43227
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 24236
56.1%
0 18991
43.9%

Length

2023-07-16T09:37:07.528925image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T09:37:07.874596image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 24236
56.1%
0 18991
43.9%

Most occurring characters

ValueCountFrequency (%)
1 24236
56.1%
0 18991
43.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43227
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 24236
56.1%
0 18991
43.9%

Most occurring scripts

ValueCountFrequency (%)
Common 43227
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 24236
56.1%
0 18991
43.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 24236
56.1%
0 18991
43.9%

loan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
36105 
1
7122 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43227
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36105
83.5%
1 7122
 
16.5%

Length

2023-07-16T09:37:08.156904image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T09:37:08.501822image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 36105
83.5%
1 7122
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 36105
83.5%
1 7122
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43227
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36105
83.5%
1 7122
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 43227
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36105
83.5%
1 7122
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36105
83.5%
1 7122
 
16.5%

contact
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
27939 
2
12556 
1
 
2732

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43227
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 27939
64.6%
2 12556
29.0%
1 2732
 
6.3%

Length

2023-07-16T09:37:08.784467image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T09:37:09.129414image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 27939
64.6%
2 12556
29.0%
1 2732
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 27939
64.6%
2 12556
29.0%
1 2732
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43227
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27939
64.6%
2 12556
29.0%
1 2732
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 43227
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27939
64.6%
2 12556
29.0%
1 2732
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27939
64.6%
2 12556
29.0%
1 2732
 
6.3%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.795591
Minimum0
Maximum30
Zeros299
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T09:37:09.447742image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median15
Q320
95-th percentile28
Maximum30
Range30
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.3425065
Coefficient of variation (CV)0.56385086
Kurtosis-1.0671919
Mean14.795591
Median Absolute Deviation (MAD)7
Skewness0.10082547
Sum639569
Variance69.597415
MonotonicityNot monotonic
2023-07-16T09:37:09.803446image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
19 2539
 
5.9%
17 2195
 
5.1%
20 1871
 
4.3%
16 1861
 
4.3%
5 1854
 
4.3%
4 1836
 
4.2%
7 1793
 
4.1%
27 1779
 
4.1%
13 1766
 
4.1%
6 1756
 
4.1%
Other values (21) 23977
55.5%
ValueCountFrequency (%)
0 299
 
0.7%
1 1241
2.9%
2 1029
2.4%
3 1386
3.2%
4 1836
4.2%
5 1854
4.3%
6 1756
4.1%
7 1793
4.1%
8 1503
3.5%
9 499
 
1.2%
ValueCountFrequency (%)
30 621
 
1.4%
29 1503
3.5%
28 1687
3.9%
27 1779
4.1%
26 1089
2.5%
25 993
2.3%
24 804
1.9%
23 432
 
1.0%
22 899
2.1%
21 873
2.0%

month
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5085016
Minimum0
Maximum11
Zeros2798
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T09:37:10.132754image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q38
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.993058
Coefficient of variation (CV)0.54335247
Kurtosis-0.98775852
Mean5.5085016
Median Absolute Deviation (MAD)2
Skewness-0.47988898
Sum238116
Variance8.958396
MonotonicityNot monotonic
2023-07-16T09:37:10.430816image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 13353
30.9%
5 6740
15.6%
1 5940
13.7%
6 5073
 
11.7%
9 3543
 
8.2%
0 2798
 
6.5%
3 2550
 
5.9%
4 1378
 
3.2%
10 673
 
1.6%
11 543
 
1.3%
Other values (2) 636
 
1.5%
ValueCountFrequency (%)
0 2798
 
6.5%
1 5940
13.7%
2 198
 
0.5%
3 2550
 
5.9%
4 1378
 
3.2%
5 6740
15.6%
6 5073
 
11.7%
7 438
 
1.0%
8 13353
30.9%
9 3543
 
8.2%
ValueCountFrequency (%)
11 543
 
1.3%
10 673
 
1.6%
9 3543
 
8.2%
8 13353
30.9%
7 438
 
1.0%
6 5073
 
11.7%
5 6740
15.6%
4 1378
 
3.2%
3 2550
 
5.9%
2 198
 
0.5%

duration
Real number (ℝ)

Distinct1556
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.42894
Minimum0
Maximum1555
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T09:37:10.807230image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q1103
median180
Q3318
95-th percentile748
Maximum1555
Range1555
Interquartile range (IQR)215

Descriptive statistics

Standard deviation238.06908
Coefficient of variation (CV)0.93569966
Kurtosis6.0133958
Mean254.42894
Median Absolute Deviation (MAD)93
Skewness2.2166832
Sum10998200
Variance56676.885
MonotonicityNot monotonic
2023-07-16T09:37:11.214411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124 181
 
0.4%
90 174
 
0.4%
112 172
 
0.4%
122 171
 
0.4%
104 170
 
0.4%
89 169
 
0.4%
121 167
 
0.4%
119 167
 
0.4%
139 165
 
0.4%
114 165
 
0.4%
Other values (1546) 41526
96.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 1
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 15
 
< 0.1%
5 33
0.1%
6 44
0.1%
7 71
0.2%
8 80
0.2%
9 74
0.2%
ValueCountFrequency (%)
1555 1
< 0.1%
1554 1
< 0.1%
1553 1
< 0.1%
1552 1
< 0.1%
1551 1
< 0.1%
1550 1
< 0.1%
1549 1
< 0.1%
1548 1
< 0.1%
1547 1
< 0.1%
1546 1
< 0.1%

campaign
Real number (ℝ)

Distinct47
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.765702
Minimum0
Maximum46
Zeros16723
Zeros (%)38.7%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T09:37:11.637701image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum46
Range46
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0749631
Coefficient of variation (CV)1.7414961
Kurtosis33.158692
Mean1.765702
Median Absolute Deviation (MAD)1
Skewness4.6540338
Sum76326
Variance9.4553979
MonotonicityNot monotonic
2023-07-16T09:37:12.029730image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 16723
38.7%
1 11979
27.7%
2 5271
 
12.2%
3 3389
 
7.8%
4 1698
 
3.9%
5 1241
 
2.9%
6 700
 
1.6%
7 514
 
1.2%
8 304
 
0.7%
9 255
 
0.6%
Other values (37) 1153
 
2.7%
ValueCountFrequency (%)
0 16723
38.7%
1 11979
27.7%
2 5271
 
12.2%
3 3389
 
7.8%
4 1698
 
3.9%
5 1241
 
2.9%
6 700
 
1.6%
7 514
 
1.2%
8 304
 
0.7%
9 255
 
0.6%
ValueCountFrequency (%)
46 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
40 3
< 0.1%
39 2
< 0.1%
38 1
 
< 0.1%
37 3
< 0.1%

pdays
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct552
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.169894
Minimum0
Maximum551
Zeros35384
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T09:37:12.421794image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile316
Maximum551
Range551
Interquartile range (IQR)0

Descriptive statistics

Standard deviation97.134225
Coefficient of variation (CV)2.4180852
Kurtosis4.9865121
Mean40.169894
Median Absolute Deviation (MAD)0
Skewness2.4464704
Sum1736424
Variance9435.0577
MonotonicityNot monotonic
2023-07-16T09:37:12.813895image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35384
81.9%
179 157
 
0.4%
89 136
 
0.3%
88 120
 
0.3%
180 119
 
0.3%
178 115
 
0.3%
367 97
 
0.2%
361 76
 
0.2%
181 76
 
0.2%
347 71
 
0.2%
Other values (542) 6876
 
15.9%
ValueCountFrequency (%)
0 35384
81.9%
1 15
 
< 0.1%
2 37
 
0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 11
 
< 0.1%
6 10
 
< 0.1%
7 7
 
< 0.1%
8 24
 
0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
551 1
< 0.1%
550 1
< 0.1%
549 1
< 0.1%
548 1
< 0.1%
547 1
< 0.1%
546 1
< 0.1%
545 1
< 0.1%
544 1
< 0.1%
543 1
< 0.1%
542 1
< 0.1%

previous
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56883429
Minimum0
Maximum39
Zeros35384
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2023-07-16T09:37:13.221086image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum39
Range39
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.8558241
Coefficient of variation (CV)3.2625039
Kurtosis73.545907
Mean0.56883429
Median Absolute Deviation (MAD)0
Skewness6.8118498
Sum24589
Variance3.444083
MonotonicityNot monotonic
2023-07-16T09:37:13.628282image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 35384
81.9%
1 2629
 
6.1%
2 2003
 
4.6%
3 1080
 
2.5%
4 672
 
1.6%
5 441
 
1.0%
6 268
 
0.6%
7 194
 
0.4%
8 128
 
0.3%
9 86
 
0.2%
Other values (30) 342
 
0.8%
ValueCountFrequency (%)
0 35384
81.9%
1 2629
 
6.1%
2 2003
 
4.6%
3 1080
 
2.5%
4 672
 
1.6%
5 441
 
1.0%
6 268
 
0.6%
7 194
 
0.4%
8 128
 
0.3%
9 86
 
0.2%
ValueCountFrequency (%)
39 1
< 0.1%
38 1
< 0.1%
37 1
< 0.1%
36 1
< 0.1%
35 1
< 0.1%
34 1
< 0.1%
33 2
< 0.1%
32 2
< 0.1%
31 1
< 0.1%
30 2
< 0.1%

poutcome
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
3
35389 
0
4662 
1
 
1750
2
 
1426

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43227
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 35389
81.9%
0 4662
 
10.8%
1 1750
 
4.0%
2 1426
 
3.3%

Length

2023-07-16T09:37:14.020817image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T09:37:14.381128image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
3 35389
81.9%
0 4662
 
10.8%
1 1750
 
4.0%
2 1426
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3 35389
81.9%
0 4662
 
10.8%
1 1750
 
4.0%
2 1426
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43227
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 35389
81.9%
0 4662
 
10.8%
1 1750
 
4.0%
2 1426
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 43227
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 35389
81.9%
0 4662
 
10.8%
1 1750
 
4.0%
2 1426
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 35389
81.9%
0 4662
 
10.8%
1 1750
 
4.0%
2 1426
 
3.3%

y
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
38239 
1
4988 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43227
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 38239
88.5%
1 4988
 
11.5%

Length

2023-07-16T09:37:14.694319image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T09:37:15.023611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 38239
88.5%
1 4988
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0 38239
88.5%
1 4988
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43227
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38239
88.5%
1 4988
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Common 43227
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38239
88.5%
1 4988
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38239
88.5%
1 4988
 
11.5%

Age Group
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
28542 
1
9559 
2
5126 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43227
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28542
66.0%
1 9559
 
22.1%
2 5126
 
11.9%

Length

2023-07-16T09:37:15.321661image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-16T09:37:15.682879image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 28542
66.0%
1 9559
 
22.1%
2 5126
 
11.9%

Most occurring characters

ValueCountFrequency (%)
0 28542
66.0%
1 9559
 
22.1%
2 5126
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43227
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28542
66.0%
1 9559
 
22.1%
2 5126
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common 43227
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28542
66.0%
1 9559
 
22.1%
2 5126
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28542
66.0%
1 9559
 
22.1%
2 5126
 
11.9%

Interactions

2023-07-16T09:36:57.861887image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:33.975210image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:37.000726image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:39.936518image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:42.891035image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:45.875574image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:48.804338image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:51.966646image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:54.898644image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:58.222483image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:34.320598image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:37.347426image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:40.285310image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:43.234869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:46.220771image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:49.169626image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:52.312199image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:55.259389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:58.554951image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:34.652781image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:37.663786image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:40.597108image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:43.564143image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:46.533838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:49.514694image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:52.610645image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:55.573212image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:58.901981image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:34.976861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:37.981200image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:40.911664image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:43.878813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:46.848006image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:49.860254image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:52.938136image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:55.902042image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:59.248628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:35.313416image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:38.295900image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:41.226249image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:44.209210image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:47.162163image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:50.206774image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:53.282628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:56.217374image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:59.579551image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:35.627515image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:38.611758image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:41.555905image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:44.524023image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:47.475839image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:50.551843image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:53.595899image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:56.528629image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:59.958714image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:35.989738image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:38.958771image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:41.917281image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:44.894666image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:47.820894image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:50.913490image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:53.956203image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:56.888814image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:37:00.289339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:36.306087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:39.274852image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:42.223840image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:45.200345image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:48.134581image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:51.259064image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:54.254611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:57.202983image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:37:00.620497image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:36.637587image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:39.583728image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:42.545421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:45.514169image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:48.448318image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:51.588886image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:54.568434image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T09:36:57.500576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-07-16T09:37:16.011732image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
agejobbalancedaymonthdurationcampaignpdayspreviousmaritaleducationdefaulthousingloancontactpoutcomeyAge Group
age1.000-0.0080.086-0.008-0.036-0.0340.039-0.017-0.0110.3290.1360.0200.2230.0650.1620.0710.1630.807
job-0.0081.0000.0220.022-0.0850.0040.015-0.012-0.0050.1900.4210.0310.2700.1020.1380.0580.1270.356
balance0.0860.0221.000-0.0020.0150.043-0.0300.0720.0820.0400.0470.2110.1020.1280.0480.0540.0990.070
day-0.0080.022-0.0021.000-0.003-0.0580.142-0.093-0.0890.0310.0390.0130.1100.0490.0920.0800.0710.065
month-0.036-0.0850.015-0.0031.0000.011-0.1450.0540.0560.0670.1010.0530.4530.1830.5100.1900.2520.130
duration-0.0340.0040.043-0.0580.0111.000-0.1100.0260.0290.0180.0140.0000.0210.0170.0320.0460.4090.032
campaign0.0390.015-0.0300.142-0.145-0.1101.000-0.113-0.1090.0200.0120.0130.0390.0150.0450.0510.0590.029
pdays-0.017-0.0120.072-0.0930.0540.026-0.1131.0000.9860.0340.0530.0370.1990.0530.2040.6060.2390.055
previous-0.011-0.0050.082-0.0890.0560.029-0.1090.9861.0000.0100.0120.0190.0280.0180.0990.2890.1020.000
marital0.3290.1900.0400.0310.0670.0180.0200.0340.0101.0000.1220.0180.0210.0520.0450.0280.0650.290
education0.1360.4210.0470.0390.1010.0140.0120.0530.0120.1221.0000.0120.1180.0780.1230.0340.0720.124
default0.0200.0310.2110.0130.0530.0000.0130.0370.0190.0180.0121.0000.0050.0750.0230.0400.0210.015
housing0.2230.2700.1020.1100.4530.0210.0390.1990.0280.0210.1180.0051.0000.0370.2150.1430.1370.184
loan0.0650.1020.1280.0490.1830.0170.0150.0530.0180.0520.0780.0750.0371.0000.0150.0540.0670.003
contact0.1620.1380.0480.0920.5100.0320.0450.2040.0990.0450.1230.0230.2150.0151.0000.2080.1500.097
poutcome0.0710.0580.0540.0800.1900.0460.0510.6060.2890.0280.0340.0400.1430.0540.2081.0000.3100.042
y0.1630.1270.0990.0710.2520.4090.0590.2390.1020.0650.0720.0210.1370.0670.1500.3101.0000.080
Age Group0.8070.3560.0700.0650.1300.0320.0290.0550.0000.2900.1240.0150.1840.0030.0970.0420.0801.000

Missing values

2023-07-16T09:37:01.170835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-16T09:37:02.068423image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomeyAge Group
0404120303410248261000301
126921094310248151000300
21521109161124876000300
329113024181024892000300
4151123091500248198000300
5174120114510248139000300
6104220136111248217000302
724202191610248380000300
840510010351024850000301
925921015071024855000300
agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomeyAge Group
45201354120149700016922601814211
452021602101471000169224000310
45203582201027000169266000312
4520455511036760001693000378011
45205792101419010169386100312
452063391201739000169972200311
452075350002637000169456100311
452085451105453000169111241813211
452093911101582001169508300301
452101921103777000169361118511100